Four Guardrails Every Developer Needs Before Deploying AI Agents to Production

A developer writing for DEV Community outlines hard lessons learned from deploying AI agents in production, including an incident where an agent sent 200 emails to the wrong recipients due to a missing validation check. The author recommends enforcing strict schema validation before any LLM output reaches a database, citing cases where models returned malformed JSON keys that corrupted records. Least-privilege database access is stressed, with agents limited to only the tables and operations they strictly require to contain potential damage from prompt hijacking. Human approval steps are advised for high-risk actions such as bulk messaging or deleting records, with the author using swipe-to-confirm interfaces and manual publish approvals in client projects. Finally, token budgets and per-minute API rate limits are recommended to prevent runaway costs, after the author experienced an uncontrolled rewrite pipeline that had to be shut down due to spiraling expenses.